This study aims to explore the possibility of using autonomous vehicles and their integration with other transport streams. A successful integration would result in better utilization of public transit resources and reduction in cost for the passengers. Trucks and buses are involved in 70% of fatal crashes in urban areas. Any reduction in the number of trucks plying on the urban roads could result in the roads becoming safer for the vulnerable road users.

Autonomous vehicles (robots and drones) can be used for last mile deliveries which could help address several problems including emissions, parking and safety as the autonomous vehicles operate at lower speeds. The navigation and prediction of paths of autonomous vehicles, such as robots and buses, is based on algorithms, GPS, and a variety of sensors. Unlike humans, these systems can process vast amounts of data in real-time. Accuracy in path prediction could smooth traffic flow and reduce congestion in urban areas. Autonomous vehicles are equipped with collision avoiding sensors which would almost zero the chances of it being involved in a fatal crash.

We start by assessing the passenger and freight demand data. Followed by planning for robot delivery, scheduling of public transport and their integration. A previous study showed that this integration would result in lower energy consumption.